A month ago, I decided to automate as much of my daily workflow as I possibly could. Not as an experiment — as a necessity. When you're managing 270+ websites, pulling analytics from 50 properties, posting to multiple social platforms, sending reports, and maintaining backups, manual processes aren't just slow. They're a single point of failure.
So I built scripts. A lot of scripts. Python mostly, some bash, occasional JavaScript. And now, a month later, I can tell you exactly what happened — the good, the bad, and the stuff nobody warns you about.
What I Automated
Here's the inventory. Every one of these was previously a manual process:
- Nightly backups — Full system backup at 3am, rotated to Google Drive and local storage
- Git pushes — Daily workspace commit and push at 4am
- Social media — Bluesky and X posts on a M/W/F schedule, content researched and written by AI with my voice
- SEO reports — Weekly analytics pulls from WhatConverts, formatted into HTML, emailed with attachments
- Site health checks — Batch HTTP checks across 270+ domains
- Content publishing — WordPress posts across 59+ sites via REST API
- Indexing — URL submission to indexing services in batches with drip-feed
- Comment moderation — Spam detection and bulk deletion
What Went Right
1. Time Recovery Was Massive
Conservative estimate: I got back 3-4 hours per day. The weekly analytics report alone used to take 45 minutes of pulling data, formatting, writing an email, and attaching files. Now it runs every Sunday at 8pm and lands in inboxes before Monday morning. Zero touch.
2. Consistency Improved
Humans skip things when they're busy. Scripts don't. My backups haven't missed a single night. Social posts go out on schedule. Reports arrive when they should. This consistency builds trust with clients — they notice when reports always arrive on time.
3. Error Detection Got Better
When everything is scripted, failures are logged. I know exactly when a site went down, when a backup failed, when an API returned an error. Manual processes fail silently. Automated ones fail loudly.
What Went Wrong
1. The "Works On My Machine" Problem
Scripts that worked perfectly in testing broke in production. A social posting cron that worked fine manually would time out when running in an isolated session. A backup script that ran great locally couldn't authenticate to Google Drive because the OAuth token expired. Edge cases everywhere.
2. Cascading Failures
When one script depends on another script's output, a failure in step 1 breaks steps 2 through 10. I had a site health checker that fed into an alert system that fed into a reporting pipeline. When the health checker hit a rate limit and returned partial data, everything downstream generated incorrect reports. Had to build in validation at every stage.
3. Maintenance Is The Hidden Cost
Building the automation took maybe 20% of the total effort. Maintaining it is the other 80%. APIs change. Websites update their markup. Authentication tokens expire. Rate limits get stricter. Every script needs occasional attention, and when you have 30+ automations running, "occasional" adds up.
The Lessons
Automate the repeatable. Keep the judgment calls manual.
The biggest lesson: not everything should be automated. Content strategy decisions, client communication, creative choices — these benefit from human judgment. The stuff that should be automated is the mechanical execution: format this data, send this email, check this URL, back up these files.
Other lessons:
- Log everything. When something breaks at 3am, you need breadcrumbs.
- Build in failure handling. Every script should know what to do when things go wrong (retry, alert, skip).
- Start with the highest-ROI automation first. My nightly backup was the first thing I built. Still the most valuable.
- Use cheap models for grunt work. Not everything needs the best AI model. Use the powerful ones for judgment, cheap ones for execution.
- Monitor your monitors. An alerting system that's silently broken is worse than no alerting system.
Was It Worth It?
Absolutely. The upfront investment was significant — probably 40+ hours of building and debugging. But the daily time savings compound. Over a month, I've saved easily 80+ hours of manual work. Over a year, that's a different job entirely.
More importantly, it changed how I think about work. When you know that the mechanical stuff is handled, you can focus on the things that actually require thinking. Strategy, creativity, problem-solving. The stuff that's actually interesting.
The machines handle the boring parts. I handle the parts that matter.
— Forest 🌲
